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Digital Filtering Performance in the ATLAS Level-1 Calorimeter Trigger David Hadley on behalf of the ATLAS Collaboration.

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Presentation on theme: "Digital Filtering Performance in the ATLAS Level-1 Calorimeter Trigger David Hadley on behalf of the ATLAS Collaboration."— Presentation transcript:

1 Digital Filtering Performance in the ATLAS Level-1 Calorimeter Trigger David Hadley on behalf of the ATLAS Collaboration

2 ATLAS Trigger and L1Calo Architecture Digital Filter Implementation Digital Filter Performance Outline

3 ATLAS Trigger and L1Calo Architecture

4 ATLAS Trigger ATLAS three level trigger: – Level 1 : hardware-based, pipelined with a fixed latency <2.5μs, maximum 75kHz accept rate. – Level 2 : software-based, seeded by L1 identified Regions of Interest. ~ 40ms mean processing time, accept rate ~ 2kHz. – Event Filter : software-based, full detector information is used. ~ 4s mean processing time. Accept rate ~ 200Hz. Data are buffered at 40MHz LHC bunch-crossing rate and stored on-detector for 2.5μs awaiting Level-1 accept. Level-1 has three subsystems: – Calorimeter Trigger (L1Calo). – Muon Trigger – Central Trigger Processor. see related talk by Johan Lundberg. 26/05/20104Digital Filter Performance - David Hadley - RT2010

5 L1Calo Architecture 26/05/2010Digital Filter Performance - David Hadley - RT20105 Analogue Calorimeter signals (7168) 8-bit trigger towers 9-bit jet elements (2x2 trigger towers) Fixed Latency <2.5μs. Massively parallel. Heavily FPGA based.

6 L1Calo Trigger ATLAS sampling calorimeters use two distinct technologies: – Liquid Argon ionisation calorimeter used in EM layer and high η hadronic layer. – Scintillating tile readout with PMTs are used in the hadronic layer η<1.5. Analogue signals from calorimeter cells are summed into 7168 approximately projective trigger towers of ηxφ ~ 0.1x0.1. Trigger algorithms operate on reduced granularity information. 26/05/2010Digital Filter Performance - David Hadley - RT20106 LAr Tiles (semi-projective segmentation )

7 L1Calo Trigger “Sliding-window” algorithms search for high-E T objects. In reality windows are processed in parallel. For example: 26/05/20107Digital Filter Performance - David Hadley - RT2010 Jet and energy-sum ECAL+HCAL Operates on jet elements 2x2 towers. Energy in window (EM+Had) > threshold. Variable window size up to 4x4. Same module does total-E T and missing-E T triggers.

8 A 7TeV Event Trigger By L1Calo 26/05/2010Digital Filter Performance - David Hadley - RT20108

9 Pre-Processor Digital Filter Implementation 26/05/2010Digital Filter Performance - David Hadley - RT20109

10 Digital Filter Trigger tower pulses are several bunch crossings wide. Need to associate trigger tower pulses with a single bunch crossing. A 5-sample digital Finite Impulse Response (FIR) filter is applied. Filter has 5 coefficients with limited 4-bit precision. Filter output: – Improved energy measurement. – Minimised effects of noise. – Improved bunch-crossing identification. 26/05/2010Digital Filter Performance - David Hadley - RT201010

11 Choosing the Filter Coefficients For white Gaussian noise, the optimum choice of filter coefficients is matched to the pulse shape. – Coefficients are proportional to the signal pulse height at each sample. Pulse shapes vary throughout the detector and each trigger tower could have its own set of filter coefficients. 7,168 towers with 5 coefficients = 35,840 free parameters! 26/05/2010Digital Filter Performance - David Hadley - RT201011

12 Choosing the Filter Coefficients Compare the performance of the following three sets of filters, where a=filter coefficients. 1.Matched Filter – each tower has a filter individually- matched to its pulse shape. 2.Common Filter – a single filter is applied to all towers in a calorimeter layer. Three sets are used: – Filters were chosen which match shapes of towers with median width. 3.Pass-through Filter – input and output are identical. 26/05/2010Digital Filter Performance - David Hadley - RT RegionFilter EM Layer(1,8,13,10,7) Hadronic Layer(1,9,15,11,5) Forward Calorimeters(0,2,13,5,0)

13 Digital Filter Performance 26/05/2010Digital Filter Performance - David Hadley - RT201013

14 Simulating the Performance The digital filter performance can be accurately simulated: – Measure the pulse shape from calibration pulses (approximately the same shape as physics pulses). – Scale the shape to produce pulses of chosen energies. – Add the simulated pulse to empty events (selected with a random trigger) containing only detector noise. 26/05/2010Digital Filter Performance - David Hadley - RT Real detector noise Calibration pulse

15 Bunch-crossing ID efficiency 1-3 GeV before reach full efficiency, depending on filter. Slower turn-on curve for Pass-through. Comparable performance for Matched and Common. Fit to extract σ of curve: 26/05/2010Digital Filter Performance - David Hadley - RT201015

16 Noise Rejection Noise PDF for a single tower (left). The total probability of noise output > 0.5GeV for all towers in the LAr EM Barrel (right). Improved noise rejection with a matched/common filter but little difference between them. 26/05/2010Digital Filter Performance - David Hadley - RT201016

17 Energy Resolution from Detector Noise Energy residual (measured-simulated) for a 25GeV pulse (left). Gaussian width of energy residual is plotted for all towers in the LAr EM barrel (right). Again, little difference between Matched and Common. Clear improvement over Pass-through. 26/05/2010Digital Filter Performance - David Hadley - RT201017

18 Optimising Usage of the Look-up Table Range Using a peak coefficient of 15 gives the best precision for matching the filter to the pulse shape. Precisely matching to individual towers pulse shape does not significantly improve performance. We can change the peak coefficient to optimise the usage of the look-up table (LUT) range. 26/05/2010Digital Filter Performance - David Hadley - RT201018

19 Choice of Filter Coefficients for Early LHC Running Based on these studies the strategy decided for running: Common filter applied across entire calorimeter layer, matched to the average pulse shape in that layer. Coefficients scaled to optimise usage of the LUT range. 26/05/2010Digital Filter Performance - David Hadley - RT RegionFilter EM Layer(1,8,13,10,7) Hadronic Layer(1,9,15,11,5) FCal(0,2,13,5,0)

20 Look-up Table E T Calibration Analogue gains are applied to scale the input pulses to 4 ADC per GeV. LUT table applies pedestal subtraction, noise cuts and final E T calibration. At present, only scale filter output back to the input scale. Initial calibration of the LUT slopes were based on calibration pulses. Preliminary checks of correlation show calibration is working well for collisions. Nominal 4 ADC input to 1 LUT output, expect a gradient ¼. 26/05/2010Digital Filter Performance - David Hadley - RT201020

21 Summary L1Calo Pre-processor digital filter shows good performance in selecting the correct bunch-crossing, rejecting noise, and improving energy resolution. The Common digital filter configuration was implemented for running. Initial cross checks of LUT calibration with collision data are encouraging. The process of understanding and optimising the trigger with collisions is continuing. We are looking forward to new physics events identified with L1Calo! 26/05/2010Digital Filter Performance - David Hadley - RT201021

22 Backup Slides 26/05/2010Digital Filter Performance - David Hadley - RT201022

23 Performance with Different Peak Coefficients BCID efficiency: Energy resolution: Noise probability:

24 L1Calo Trigger (in full) “Sliding-window” algorithms search for high-E T objects. In reality windows are processed in parallel. Central cluster > threshold. Hadronic and e.m. isolation requirements in surrounding towers. e/gamma and τ/hadronJet and energy-sum ECAL+HCAL Operates on jet elements 2x2 towers. Energy in window (EM+Had) > threshold. Variable window size. Same module does total-E T and missing-E T triggers. Double counting is avoided by requiring a local E T maximum. 26/05/201024Digital Filter Performance - David Hadley - RT2010


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